def test_metrics():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = get_config()
# turn off sta/lta check and snr checks
newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
stream1 = processed_streams[0]
stream2 = processed_streams[1]
summary1 = StationSummary.from_config(stream1)
summary2 = StationSummary.from_config(stream2)
workspace.setStreamMetrics(event.id, 'processed', summary1)
workspace.setStreamMetrics(event.id, 'processed', summary2)
workspace.calcStationMetrics(event.id, labels=['processed'])
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.station,
'processed')
s1_df_in = summary1.pgms.sort_values(['IMT', 'IMC'])
s1_df_out = summary1_a.pgms.sort_values(['IMT', 'IMC'])
array1 = s1_df_in['Result'].as_matrix()
array2 = s1_df_out['Result'].as_matrix()
np.testing.assert_almost_equal(array1, array2, decimal=4)
df = workspace.getMetricsTable(event.id)
cmp_series = {
'GREATER_OF_TWO_HORIZONTALS': 0.6787,
'H1': 0.3869,
'H2': 0.6787,
'Z': 0.7663
}
pga_dict = df.iloc[0]['PGA'].to_dict()
for key, value in pga_dict.items():
value2 = cmp_series[key]
np.testing.assert_almost_equal(value, value2, decimal=4)
workspace.close()
except Exception as e:
raise(e)
finally:
shutil.rmtree(tdir)
def test_metrics():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet',
eventid,
'*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = get_config()
# turn off sta/lta check and snr checks
newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
stream1 = processed_streams[0]
stream2 = processed_streams[1]
summary1 = StationSummary.from_config(stream1)
summary2 = StationSummary.from_config(stream2)
workspace.setStreamMetrics(event.id, 'processed', summary1)
workspace.setStreamMetrics(event.id, 'processed', summary2)
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.station,
'processed')
s1_df_in = summary1.pgms.sort_values(['IMT', 'IMC'])
s1_df_out = summary1_a.pgms.sort_values(['IMT', 'IMC'])
array1 = s1_df_in['Result'].as_matrix()
array2 = s1_df_out['Result'].as_matrix()
np.testing.assert_almost_equal(array1, array2, decimal=4)
df = workspace.getMetricsTable(event.id)
cmp_series = {'GREATER_OF_TWO_HORIZONTALS': 0.6787,
'HN1': 0.3869,
'HN2': 0.6787,
'HNZ': 0.7663}
pga_dict = df.iloc[0]['PGA'].to_dict()
for key, value in pga_dict.items():
value2 = cmp_series[key]
np.testing.assert_almost_equal(value, value2, decimal=4)
workspace.close()
except Exception as e:
raise(e)
finally:
shutil.rmtree(tdir)
def test_workspace():
eventid = 'us1000778i'
datafiles, origin = read_data_dir('geonet', eventid, '*.V1A')
event = get_event_object(origin)
tdir = tempfile.mkdtemp()
try:
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=H5pyDeprecationWarning)
warnings.filterwarnings("ignore", category=YAMLLoadWarning)
warnings.filterwarnings("ignore", category=FutureWarning)
config = get_config()
tfile = os.path.join(tdir, 'test.hdf')
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
workspace = StreamWorkspace(tfile)
t1 = time.time()
workspace.addStreams(event, raw_streams, label='raw')
t2 = time.time()
print('Adding %i streams took %.2f seconds' % (len(raw_streams),
(t2 - t1)))
str_repr = workspace.__repr__()
assert str_repr == 'Events: 1 Stations: 3 Streams: 3'
eventobj = workspace.getEvent(eventid)
assert eventobj.origins[0].latitude == event.origins[0].latitude
assert eventobj.magnitudes[0].mag == event.magnitudes[0].mag
stations = workspace.getStations()
assert sorted(stations) == ['hses', 'thz', 'wtmc']
stations = workspace.getStations(eventid=eventid)
assert sorted(stations) == ['hses', 'thz', 'wtmc']
# test retrieving tags for an event that doesn't exist
try:
workspace.getStreamTags('foo')
except KeyError:
assert 1 == 1
# test retrieving event that doesn't exist
try:
workspace.getEvent('foo')
except KeyError:
assert 1 == 1
instream = None
for stream in raw_streams:
if stream[0].stats.station.lower() == 'hses':
instream = stream
break
if instream is None:
assert 1 == 2
outstream = workspace.getStreams(eventid,
stations=['hses'],
labels=['raw'])[0]
compare_streams(instream, outstream)
label_summary = workspace.summarizeLabels()
assert label_summary.iloc[0]['Label'] == 'raw'
assert label_summary.iloc[0]['Software'] == 'gmprocess'
sc = StreamCollection(raw_streams)
processed_streams = process_streams(sc, origin, config=config)
workspace.addStreams(event, processed_streams, 'processed')
idlist = workspace.getEventIds()
assert idlist[0] == eventid
event_tags = workspace.getStreamTags(eventid)
assert sorted(event_tags) == [
'hses_processed', 'hses_raw', 'thz_processed', 'thz_raw',
'wtmc_processed', 'wtmc_raw'
]
outstream = workspace.getStreams(eventid,
stations=['hses'],
labels=['processed'])[0]
provenance = workspace.getProvenance(eventid, labels=['processed'])
first_row = pd.Series({
'Record': 'NZ.HSES.HN1',
'Processing Step': 'Remove Response',
'Step Attribute': 'input_units',
'Attribute Value': 'counts'
})
last_row = pd.Series({
'Record': 'NZ.WTMC.HNZ',
'Processing Step': 'Detrend',
'Step Attribute': 'detrending_method',
'Attribute Value': 'baseline_sixth_order'
})
assert provenance.iloc[0].equals(first_row)
assert provenance.iloc[-1].equals(last_row)
# compare the parameters from the input processed stream
# to it's output equivalent
instream = None
for stream in processed_streams:
if stream[0].stats.station.lower() == 'hses':
instream = stream
break
if instream is None:
assert 1 == 2
compare_streams(instream, outstream)
workspace.close()
# read in data from a second event and stash it in the workspace
eventid = 'nz2018p115908'
datafiles, origin = read_data_dir('geonet', eventid, '*.V2A')
raw_streams = []
for dfile in datafiles:
raw_streams += read_data(dfile)
event = get_event_object(origin)
workspace = StreamWorkspace.open(tfile)
workspace.addStreams(event, raw_streams, label='foo')
stations = workspace.getStations(eventid)
eventids = workspace.getEventIds()
assert eventids == ['us1000778i', 'nz2018p115908']
instation = raw_streams[0][0].stats.station
this_stream = workspace.getStreams(eventid,
stations=[instation],
labels=['foo'])[0]
assert instation == this_stream[0].stats.station
# set and retrieve waveform metrics in the file
imclist = [
'greater_of_two_horizontals', 'channels', 'rotd50', 'rotd100'
]
imtlist = ['sa1.0', 'PGA', 'pgv', 'fas2.0', 'arias']
usid = 'us1000778i'
tags = workspace.getStreamTags(usid)
workspace.setStreamMetrics(eventid,
labels=['foo'],
imclist=imclist,
imtlist=imtlist)
summary = workspace.getStreamMetrics(eventid, instation, 'foo')
summary_series = summary.toSeries()['ARIAS']
cmpseries = pd.Series({
'GEOMETRIC_MEAN': np.NaN,
'GREATER_OF_TWO_HORIZONTALS': 0.0005,
'HN1': 0.0001,
'HN2': 0.0005,
'HNZ': 0.0000,
'ROTD100.0': 0.0005,
'ROTD50.0': 0.0003
})
assert cmpseries.equals(summary_series)
workspace.setStreamMetrics(usid, labels=['processed'])
df = workspace.getMetricsTable(usid, labels=['processed'])
cmpdict = {
'GREATER_OF_TWO_HORIZONTALS': [26.8906, 4.9415, 94.6646],
'HN1': [24.5105, 4.9415, 94.6646],
'HN2': [26.8906, 4.0758, 86.7877],
'HNZ': [16.0941, 2.5401, 136.7054]
}
cmpframe = pd.DataFrame(cmpdict)
assert df['PGA'].equals(cmpframe)
inventory = workspace.getInventory(usid)
codes = [
station.code for station in inventory.networks[0].stations
]
assert sorted(codes) == ['HSES', 'THZ', 'WPWS', 'WTMC']
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)